Modelling Census Data

Pa-Shun Hawkins & Robert J. Dellinger

Summer Research Project 2024

Load Libraries

This document shows how to use and download US Census data to research the underlying drivers of emissions output from industrial facilities across seven states identified as primary contributors to acid rain-related changes at the Hubbard Brook Experimental Forest (HBEF). The aim is to investigate if there is a relationship between wealth inequality and emissions output from these facilities. Census data provides important demographic and economic context, which helps to explore patterns of industrial emissions and their potential connections with socio-economic variables, such as wealth inequality, across regions.

The analysis in this document introduces a series of frameworks that leverage Census data. This section looks at segregation and diversity indices, which help to understand demographic patterns that may correlate with the distribution of industrial emissions.

Indices of Segregation and Diversity

A large body of research in the social sciences is concerned with neighborhood segregation and diversity. Segregation generally refers to the extent to which two or more groups live apart from each other; diversity measures neighborhood heterogeneity among groups. A wide range of indices have been developed by social scientists to measure segregation and diversity, often relying on spatial Census data. Segregation and diversity indices are implemented in a variety of R packages; the package recommended by this document is the segregation package (Elbers 2021), which includes R functions for various regional and local indices

Data Retrieval

The data setup below uses spatial methods to acquire Census tract-level data on population estimates for non-Hispanic white, non-Hispanic black, non-Hispanic Asian, and Hispanic populations across the seven states identified as major contributors to acid rain at HBEF: Ohio, Pennsylvania, Tennessee, West Virginia, Kentucky, Indiana, and Illinois. The analysis filters Census tracts that intersect the largest urbanized areas by population in each state using an inner spatial join. This prelminary data exploration uses urbanized areas for 2019 and data from the 2015-2019 ACS. To extend this analysis to multiple states, a vector of state abbreviations is used, iterating over them to append the data for each state.

# Define the states for analysis
states <- c("OH", "PA", "TN", "WV", "KY", "IN", "IL")

# Initialize an empty list to store data for each state
state_data_list <- list()

# Loop through each state to get tract data by race/ethnicity
for (state in states) {
  state_data <- get_acs(
    geography = "tract",
    variables = c(
      white = "B03002_003",
      black = "B03002_004",
      asian = "B03002_006",
      hispanic = "B03002_012"
    ),
    state = state,
    geometry = TRUE,
    year = 2019
  )
  state_data_list[[state]] <- state_data
}

# Combine data from all states
acs_data <- bind_rows(state_data_list)

# Use tidycensus to get urbanized areas by population with geometry, 
# then filter for those that have populations of 100,000 or more
us_urban_areas <- get_acs(
  geography = "urban area",
  variables = "B01001_001",
  geometry = TRUE,
  year = 2019,
  survey = "acs1"
) %>%
  filter(estimate >= 100000) %>%
  transmute(urban_name = str_remove(NAME, 
                                    fixed(" Urbanized Area (2010)")))

# Compute an inner spatial join between tract data and urbanized areas
urban_data <- acs_data %>%
  st_join(us_urban_areas, left = FALSE) %>%
  select(-NAME) %>%
  st_drop_geometry() %>% 
  mutate(dataset = "2015-2019 5-year ACS")


# Get tracts for each state and bind them into a single dataset
tracts_data <- bind_rows(
  tracts(state = "WV", cb = TRUE, year = 2019),
  tracts(state = "PA", cb = TRUE, year = 2019),
  tracts(state = "OH", cb = TRUE, year = 2019),
  tracts(state = "TN", cb = TRUE, year = 2019),
  tracts(state = "KY", cb = TRUE, year = 2019),
  tracts(state = "IN", cb = TRUE, year = 2019),
  tracts(state = "IL", cb = TRUE, year = 2019)
) %>%
  mutate(State = case_when(
    STATEFP == "54" ~ "WV",
    STATEFP == "42" ~ "PA",
    STATEFP == "39" ~ "OH",
    STATEFP == "47" ~ "TN",
    STATEFP == "21" ~ "KY",
    STATEFP == "18" ~ "IN",
    STATEFP == "17" ~ "IL"
  ))

# Display the first few rows using flextable
flextable(urban_data %>% head(10) %>%
  dplyr::select(-dataset)) %>%
  set_caption("Census Tract-Level Data (Urbanized Areas), 5-year ACS") %>%
  autofit() %>%
  colformat_num(j = c("estimate", "moe"), digits = 2) %>%  # Format numerical columns
  set_header_labels(
    GEOID = "Census Tract GEOID",
    variable = "Population Group",
    estimate = "Population Estimate",
    moe = "Margin of Error",
    urban_name = "Urban Area"
  )

The spatial analysis workflow uses the following steps: 1. Data on race and ethnicity from the 2015-2019 ACS for the largest demographic groups is acquired at the Census tract level for multiple states. 2. Urban areas for the entire US are obtained, filtered to those with populations of 750,000 or greater. 3. A spatial join between the Census tract data and urban area data is computed, retaining only those Census tracts intersecting the urban area boundaries.

Dissimilarity Index

The dissimilarity index is widely used to assess neighborhood segregation between two groups within a region. It ranges from 0 (perfect integration) to 1 (complete segregation). The dissimilarity index (\(D\)) is calculated as follows:

The dissimilarity index (\(D\)) is calculated as follows:

\[ D = \frac{1}{2} \sum_{i=1}^{N} \left| \frac{a_i}{A} - \frac{b_i}{B} \right| \]

Where: - \(a_i\) represents the population of group A in a given unit \(i\) - \(A\) is the total population of group A in the region - \(b_i\) represents the population of group B in a given unit \(i\) - \(B\) is the total population of group B in the region

The dissimilarity index can be computed using the dissimilarity() function from the segregation package.

# Compute dissimilarity index between Black and white populations
dissimilarity_index_data_black_white <- urban_data %>%
  filter(variable %in% c("white", "black")) %>%
  group_by(urban_name) %>%
  group_modify(~
    dissimilarity(.x,
      group = "variable",
      unit = "GEOID",
      weight = "estimate"
    )
  ) %>% 
  arrange(desc(est))

# Present results in a table
flextable(dissimilarity_index_data_black_white) %>%
  set_caption("Dissimilarity Index between Black and White Populations") %>%
  colformat_num(j = c("est"), digits = 3) %>%
  set_header_labels(
    urban_name = "Urban Area",
    stat = "Statistic",
    est = "Dissimilarity Index Estimate"
  ) %>%
  autofit()
Dissimilarity Index between Black and White Populations

Urban Area

Statistic

Dissimilarity Index Estimate

Chicago, IL--IN

D

0.7484

Philadelphia, PA--NJ--DE--MD

D

0.7325

Cleveland, OH

D

0.7298

Peoria, IL

D

0.6811

Memphis, TN--MS--AR

D

0.6596

Cincinnati, OH--KY--IN

D

0.6552

Pittsburgh, PA

D

0.6551

Harrisburg, PA

D

0.6395

Akron, OH

D

0.6300

Dayton, OH

D

0.6294

Youngstown, OH--PA

D

0.6249

Indianapolis, IN

D

0.6238

Erie, PA

D

0.6100

Toledo, OH--MI

D

0.5944

Columbus, OH

D

0.5720

Canton, OH

D

0.5665

Johnson City, TN

D

0.5651

Chattanooga, TN--GA

D

0.5624

St. Louis, MO--IL

D

0.5611

Fort Wayne, IN

D

0.5583

Scranton, PA

D

0.5577

Knoxville, TN

D

0.5549

Rockford, IL

D

0.5500

South Bend, IN--MI

D

0.5484

Charleston, WV

D

0.5427

Lancaster, PA

D

0.5415

Louisville/Jefferson County, KY--IN

D

0.5399

Nashville-Davidson, TN

D

0.5312

Pottstown, PA

D

0.5272

Springfield, IL

D

0.5261

Huntington, WV--KY--OH

D

0.5195

York, PA

D

0.5130

Elkhart, IN--MI

D

0.5102

Allentown, PA--NJ

D

0.5062

Reading, PA

D

0.5047

Evansville, IN--KY

D

0.4931

Davenport, IA--IL

D

0.4881

Kenosha, WI--IL

D

0.4819

Round Lake Beach--McHenry--Grayslake, IL--WI

D

0.4701

Lorain--Elyria, OH

D

0.4687

Lexington-Fayette, KY

D

0.4627

Champaign, IL

D

0.4034

Bloomington--Normal, IL

D

0.3644

Bloomington, IN

D

0.3391

Lafayette, IN

D

0.3318

Murfreesboro, TN

D

0.3285

Clarksville, TN--KY

D

0.3076

Hagerstown, MD--WV--PA

D

0.2474

Binghamton, NY--PA

D

0.2178

Trenton, NJ

D

0.0000

# Compute dissimilarity index between non-Hispanic white and Hispanic populations
dissimilarity_index_data_hispanic_white <- urban_data %>%
  filter(variable %in% c("white", "hispanic")) %>%
  group_by(urban_name) %>%
  group_modify(~
    dissimilarity(.x,
      group = "variable",
      unit = "GEOID",
      weight = "estimate"
    )
  ) %>% 
  arrange(desc(est))

print(dissimilarity_index_data_hispanic_white)
## # A tibble: 50 × 3
## # Groups:   urban_name [50]
##    urban_name                   stat    est
##    <chr>                        <chr> <dbl>
##  1 Reading, PA                  D     0.636
##  2 Memphis, TN--MS--AR          D     0.589
##  3 Philadelphia, PA--NJ--DE--MD D     0.573
##  4 Allentown, PA--NJ            D     0.563
##  5 Chicago, IL--IN              D     0.553
##  6 Erie, PA                     D     0.517
##  7 Youngstown, OH--PA           D     0.510
##  8 Cleveland, OH                D     0.503
##  9 York, PA                     D     0.496
## 10 Scranton, PA                 D     0.489
## # ℹ 40 more rows
# Present results in a table
flextable(dissimilarity_index_data_hispanic_white) %>%
  set_caption("Dissimilarity Index between Hispanic and White Populations") %>%
  colformat_num(j = c("est"), digits = 3) %>%
  set_header_labels(
    urban_name = "Urban Area",
    stat = "Statistic",
    est = "Dissimilarity Index Estimate"
  ) %>%
  autofit()
Dissimilarity Index between Hispanic and White Populations

Urban Area

Statistic

Dissimilarity Index Estimate

Reading, PA

D

0.6364

Memphis, TN--MS--AR

D

0.5889

Philadelphia, PA--NJ--DE--MD

D

0.5730

Allentown, PA--NJ

D

0.5631

Chicago, IL--IN

D

0.5529

Erie, PA

D

0.5166

Youngstown, OH--PA

D

0.5105

Cleveland, OH

D

0.5034

York, PA

D

0.4965

Scranton, PA

D

0.4892

Lorain--Elyria, OH

D

0.4855

Fort Wayne, IN

D

0.4840

Nashville-Davidson, TN

D

0.4827

Indianapolis, IN

D

0.4798

Huntington, WV--KY--OH

D

0.4765

Lancaster, PA

D

0.4589

Charleston, WV

D

0.4531

South Bend, IN--MI

D

0.4516

Akron, OH

D

0.4489

Harrisburg, PA

D

0.4468

Chattanooga, TN--GA

D

0.4416

Cincinnati, OH--KY--IN

D

0.4383

Columbus, OH

D

0.4331

Peoria, IL

D

0.4238

Lexington-Fayette, KY

D

0.4137

Rockford, IL

D

0.4076

Evansville, IN--KY

D

0.4038

Canton, OH

D

0.4007

Elkhart, IN--MI

D

0.3995

Louisville/Jefferson County, KY--IN

D

0.3976

Pittsburgh, PA

D

0.3883

Springfield, IL

D

0.3858

Round Lake Beach--McHenry--Grayslake, IL--WI

D

0.3759

Knoxville, TN

D

0.3757

Dayton, OH

D

0.3710

Johnson City, TN

D

0.3673

St. Louis, MO--IL

D

0.3628

Toledo, OH--MI

D

0.3424

Davenport, IA--IL

D

0.3393

Bloomington, IN

D

0.3317

Bloomington--Normal, IL

D

0.3265

Murfreesboro, TN

D

0.3254

Clarksville, TN--KY

D

0.3223

Lafayette, IN

D

0.3089

Binghamton, NY--PA

D

0.2939

Pottstown, PA

D

0.2910

Champaign, IL

D

0.2402

Hagerstown, MD--WV--PA

D

0.2152

Kenosha, WI--IL

D

0.1627

Trenton, NJ

D

0.0000

One disadvantage of the dissimilarity index is that it only measures segregation between two groups, the above code shows the dissimilarity index between white and Hispanic and white and Black populations in the identified states.

Multi-Group Segregation Indices

Computing multi-group indices is achieved using the segregation package. To measure segregation and diversity across multiple groups, we use the mutual_within() function computes indices like the Mutual Information Index (M) and Theil’s Entropy Index (H).

The Mutual Information Index (\(M\)) is computed as follows (Elbers, 2021):

\[ M(T) = \sum_{u=1}^{U} \sum_{g=1}^{G} p_{ug} \log \frac{p_{ug}}{p_u p_g} \]

Where: - \(U\) is the total number of units - \(G\) is the total number of groups - \(p_{ug}\) is the joint probability of being in unit \(u\) and group \(g\)

Theil’s Entropy Index (\(H\)) is calculated as (Mora and Ruiz-Castillo, 2011):

\[ H(T) = \frac{M(T)}{E(T)} \]

Where \(E(T)\) is the entropy of the dataset, normalizing \(H\) to range between values of 0 and 1.

# Compute multi-group segregation indices
multigroup_indices <- mutual_within(
  data = urban_data,
  group = "variable",
  unit = "GEOID",
  weight = "estimate",
  within = "urban_name",
  wide = TRUE
)

# present the results in a table
flextable(multigroup_indices %>% head(15))  %>%
  set_caption("Multi-Group Segregation Indices by Urban Area") %>%
  colformat_num(j = c("M", "p", "H", "ent_ratio"), digits = 3) %>%
  set_header_labels(
    urban_name = "Urban Area",
    M = "Mutual Information (M)",
    p = "Proportion (p)",
    H = "Entropy (H)",
    ent_ratio = "Entropy Ratio"
  ) %>%
  autofit()
Multi-Group Segregation Indices by Urban Area

Urban Area

Mutual Information (M)

Proportion (p)

Entropy (H)

Entropy Ratio

Akron, OH

0.169103

0.0173495

0.28950

0.6300

Allentown, PA--NJ

0.179233

0.0205808

0.23064

0.8382

Binghamton, NY--PA

0.006391

0.0002773

0.05433

0.1269

Bloomington, IN

0.061115

0.0037081

0.11146

0.5914

Bloomington--Normal, IL

0.069118

0.0042043

0.09682

0.7700

Canton, OH

0.095663

0.0093755

0.22571

0.4572

Champaign, IL

0.136618

0.0042517

0.13280

1.1096

Charleston, WV

0.072811

0.0056961

0.19668

0.3993

Chattanooga, TN--GA

0.213702

0.0094675

0.26708

0.8631

Chicago, IL--IN

0.438394

0.2347320

0.37413

1.2639

Cincinnati, OH--KY--IN

0.209106

0.0479094

0.30302

0.7443

Clarksville, TN--KY

0.076244

0.0054987

0.07999

1.0281

Cleveland, OH

0.326898

0.0509027

0.39937

0.8829

Columbus, OH

0.208010

0.0426960

0.23622

0.9498

Davenport, IA--IL

0.126018

0.0040899

0.15933

0.8531

Localized Segregation Indices

# Calculate local segregation indices
local_segregation_indices <- urban_data %>%
  mutual_local(
    group = "variable",
    unit = "GEOID",
    weight = "estimate", 
    wide = TRUE
  )

# Present local segregation indices
flextable(local_segregation_indices) %>%
  set_caption("Local Segregation Indices by Census Tract") %>%
  colformat_num(j = c("ls", "p"), digits = 5) %>%
  set_header_labels(
    GEOID = "Census Tract GEOID",
    ls = "Local Segregation Index (ls)",
    p = "Proportion (p)"
  ) %>%
  autofit()
Local Segregation Indices by Census Tract

Census Tract GEOID

Local Segregation Index (ls)

Proportion (p)

17007010100

0.401218

0.0002023044

17007010200

0.269342

0.0001634530

17007010300

0.385884

0.0001796298

17007010400

0.171788

0.0001788957

17007010500

0.177973

0.0002794362

17007010601

0.241287

0.0001033952

17007010602

0.073872

0.0003204897

17019000200

0.943404

0.0000507052

17019000301

0.304861

0.0001359662

17019000302

1.120082

0.0000715039

17019000401

0.308713

0.0001387665

17019000402

0.142073

0.0001118234

17019000500

0.057330

0.0001011114

17019000700

0.124053

0.0000841190

17019000800

0.188129

0.0001650843

17019000901

0.375315

0.0001355584

17019000902

0.046774

0.0001799560

17019001000

0.125515

0.0001172610

17019001100

0.047190

0.0000977129

17019001201

0.120210

0.0001768022

17019001203

0.047426

0.0001308005

17019001204

0.307643

0.0001042380

17019001205

0.278896

0.0001941752

17019001206

0.084887

0.0000554903

17019001301

0.063861

0.0001668787

17019001302

0.192637

0.0001195991

17019001400

0.296565

0.0001813426

17019005300

0.577137

0.0001440681

17019005401

0.028646

0.0001149500

17019005402

0.070371

0.0000935804

17019005500

0.025896

0.0001263417

17019005600

0.027407

0.0001875686

17019005701

0.038886

0.0001318064

17019005702

0.266121

0.0000909976

17019005800

0.138622

0.0001001870

17019005900

0.460765

0.0001623927

17019006000

0.424573

0.0000941513

17019010604

0.235767

0.0000628310

17019010900

0.202306

0.0002493665

17019011000

0.033470

0.0001144606

17019011100

0.633577

0.0000600578

17031010100

0.261512

0.0001214479

17031010201

0.267014

0.0001839798

17031010202

0.299388

0.0000750655

17031010300

0.128723

0.0001688634

17031010400

0.013687

0.0001386034

17031010501

0.116221

0.0001054887

17031010502

0.054898

0.0000817265

17031010503

0.093802

0.0000472524

17031010600

0.087745

0.0001689450

17031010701

0.175266

0.0000976314

17031010702

0.278950

0.0001352049

17031020100

0.286537

0.0000936891

17031020200

0.206919

0.0001775635

17031020301

0.121155

0.0001416756

17031020302

0.088924

0.0001323230

17031020400

0.193530

0.0001104640

17031020500

1.031769

0.0001701412

17031020601

0.375577

0.0001682381

17031020602

0.362149

0.0001271029

17031020701

0.163935

0.0000492915

17031020702

0.458535

0.0002211727

17031020801

0.570757

0.0001571727

17031020802

0.636000

0.0001862364

17031020901

0.532747

0.0001580698

17031020902

0.397462

0.0001298217

17031030101

0.236526

0.0001030961

17031030102

0.100694

0.0000713408

17031030103

0.061553

0.0000578013

17031030104

0.123069

0.0000905897

17031030200

0.055491

0.0001506748

17031030300

0.318750

0.0000949942

17031030400

0.199705

0.0000675617

17031030500

0.105851

0.0001748447

17031030601

0.249586

0.0000898285

17031030603

0.201331

0.0000629941

17031030604

0.248083

0.0000985014

17031030701

0.106914

0.0000428480

17031030702

0.412632

0.0000581275

17031030703

0.019569

0.0000781105

17031030706

0.168851

0.0000768327

17031030800

0.108661

0.0001108990

17031030900

0.063499

0.0000817265

17031031000

0.086899

0.0001045099

17031031100

0.115195

0.0001233238

17031031200

0.321507

0.0001667428

17031031300

0.198684

0.0002076604

17031031400

0.087213

0.0001302567

17031031501

0.265413

0.0001192457

17031031502

0.250075

0.0001222635

17031031700

0.022508

0.0001772916

17031031800

0.106501

0.0000502702

17031031900

0.026143

0.0000655770

17031032100

0.085078

0.0001869705

17031040100

0.120199

0.0001096212

17031040201

0.153662

0.0001777266

17031040202

0.278538

0.0001895805

17031040300

0.514401

0.0000784368

17031040401

0.136393

0.0000849075

17031040402

0.109128

0.0001339815

17031040600

0.085264

0.0000680511

17031040700

0.114795

0.0000868650

17031040800

0.090214

0.0000425761

17031040900

0.135669

0.0000502159

17031050100

0.097534

0.0000706067

17031050200

0.123510

0.0001314802

17031050300

0.223635

0.0000663926

17031050500

0.189426

0.0001548345

17031050600

0.198263

0.0000574750

17031050700

0.214693

0.0000464096

17031050800

0.100044

0.0000369482

17031050900

0.189708

0.0000372473

17031051000

0.192519

0.0000486118

17031051100

0.132557

0.0000436636

17031051200

0.055879

0.0000401020

17031051300

0.114923

0.0000766424

17031051400

0.045109

0.0000537503

17031060100

0.063367

0.0000760715

17031060200

0.133324

0.0000589431

17031060300

0.128447

0.0000777299

17031060400

0.150930

0.0001023077

17031060500

0.139578

0.0000320816

17031060800

0.108364

0.0001293867

17031060900

0.029893

0.0001949093

17031061000

0.123304

0.0000589975

17031061100

0.126272

0.0000370026

17031061200

0.127914

0.0000563331

17031061500

0.144445

0.0000522549

17031061800

0.093823

0.0000357520

17031061901

0.230917

0.0000932813

17031061902

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0.080083

0.0002824268

54003971300

0.070095

0.0002605407

54003971400

0.085936

0.0002366426

54003971500

0.046163

0.0000877078

54003971600

0.057359

0.0001212576

54003971700

0.092970

0.0001436331

54003971800

0.324175

0.0002225593

54003971900

0.083408

0.0003012408

54003972000

0.072328

0.0003394668

54003972101

0.083138

0.0001478472

54003972102

0.095028

0.0003228279

54011000101

0.186081

0.0000442617

54011000102

0.134389

0.0000574206

54011000200

0.340457

0.0000749024

54011000300

0.296114

0.0000682142

54011000400

0.149014

0.0000598947

54011000500

0.072636

0.0000819984

54011000600

0.048217

0.0000372201

54011000900

0.155455

0.0000377095

54011001000

0.245802

0.0000513034

54011001100

0.209624

0.0000520103

54011001200

0.134886

0.0000755005

54011001300

0.093680

0.0000647341

54011001400

0.185853

0.0000629126

54011001500

0.220231

0.0000359423

54011001600

0.149697

0.0000315378

54011001800

0.117059

0.0000965711

54011001900

0.191371

0.0000599491

54011002000

0.266251

0.0000762618

54011002100

0.310806

0.0000841734

54011010102

0.237301

0.0001593205

54011010201

0.231883

0.0001607886

54011010202

0.194171

0.0001014649

54011010300

0.264864

0.0000738964

54011010400

0.194551

0.0001728600

54011010500

0.325480

0.0001595924

54011010600

0.307891

0.0001063315

54011010700

0.342839

0.0001916196

54011010800

0.368828

0.0001791132

54011010900

0.049899

0.0000408904

54037972201

0.110229

0.0001300120

54037972300

0.106987

0.0001244385

54039000100

0.146586

0.0000325710

54039000200

0.130696

0.0000547018

54039000300

0.265890

0.0000732711

54039000500

0.204598

0.0000575838

54039000600

0.127263

0.0000949670

54039000700

0.498395

0.0000568497

54039000800

0.060571

0.0000408633

54039000900

0.084263

0.0000299881

54039001100

0.086532

0.0001203060

54039001200

0.166579

0.0000414070

54039001300

0.080809

0.0000580731

54039001500

0.109926

0.0001178591

54039001700

0.132143

0.0000438267

54039001800

0.076513

0.0000691114

54039001901

0.230303

0.0000935260

54039001902

0.260108

0.0000986101

54039002000

0.188734

0.0000753374

54039002100

0.135136

0.0001206323

54039010100

0.049436

0.0000908616

54039010200

0.080677

0.0000577197

54039010300

0.164846

0.0000586169

54039010400

0.435035

0.0000370026

54039010500

0.061992

0.0001173153

54039010600

0.335744

0.0001203604

54039010701

0.161934

0.0001244929

54039010702

0.225757

0.0001275380

54039010900

0.374206

0.0000757180

54039011000

0.288545

0.0001225898

54039011100

0.303765

0.0001264776

54039011200

0.327642

0.0001082890

54039011301

0.314913

0.0000903994

54039011302

0.320372

0.0001600274

54039011401

0.260671

0.0000777299

54039011402

0.312584

0.0001005133

54039011500

0.104560

0.0001070655

54039011800

0.269055

0.0001113340

54039012100

0.242773

0.0001094853

54039012200

0.263501

0.0001490707

54039012300

0.244771

0.0001901786

54039012800

0.103037

0.0001058693

54039012900

0.104664

0.0000296619

54039013000

0.163147

0.0001195719

54039013100

0.093194

0.0001014649

54039013200

0.276872

0.0001021446

54039013300

0.239066

0.0000712320

54039013400

0.230496

0.0000511674

54039013500

0.293907

0.0000669092

54039013600

0.252770

0.0001128294

54039013701

0.247288

0.0000521190

54039013702

0.292894

0.0001432525

54039013800

0.219685

0.0000705251

54079020100

0.374206

0.0001581242

54079020200

0.249462

0.0001278370

54079020300

0.337538

0.0002880819

54079020400

0.263780

0.0003568670

54079020500

0.291663

0.0001780529

54079020601

0.177825

0.0003165203

54079020603

0.212310

0.0003645884

54079020604

0.152007

0.0000689483

54079020605

0.329265

0.0002025219

54079020700

0.348569

0.0001216654

54099005100

0.237817

0.0000508684

54099005200

0.329822

0.0000473340

54099020100

0.161105

0.0000798777

54099020300

0.263845

0.0001213119

54099020400

0.352467

0.0001712831

54099020500

0.328512

0.0001317249

54099020600

0.344249

0.0001239492

54099020700

0.367199

0.0000993986

# Join Census tract data with local segregation indices on GEOID
tracts_segregation_indices <- tracts_data %>%
  # Make sure both GEOID columns are of the same type
  mutate(GEOID = as.character(GEOID)) %>%
  inner_join(local_segregation_indices %>% mutate(GEOID = as.character(GEOID)), by = "GEOID")

# Function to generate segregation plot for a given state
plot_segregation <- function(state_code, tracts_data) {
  tracts_segregation_indices %>%
    filter(State == state_code) %>%
    ggplot(aes(fill = ls)) + 
    geom_sf(color = "white", linewidth = 0.05) +  # Add tract boundaries for better clarity
    # NAD83 / Conus Albers (EPSG:5070) projection for better accuracy in the U.S.
    coord_sf(crs = 5070) +
    # Use viridis color scale for better perceptual uniformity
    scale_fill_viridis_c(
  option = "inferno", 
  name = "Segregation Index", 
  limits = c(0, 2),  # Adjust based on data range
  breaks = c(0.5, 1, 1.5, 2)) +
    theme_minimal() +  # Cleaner theme
    theme(
      plot.title = element_text(hjust = 0.5, size = 14),  # Center the title
      axis.text =  element_text(size = 5),  # Remove axis text
      legend.position = "right",  # Move the legend to the side
      legend.title = element_text(size = 10),  # Adjust legend title
      legend.text = element_text(size = 8),    # Adjust legend text size
      plot.margin = margin(10, 10, 10, 10)     # Add margins to avoid plot overlap
    ) +
    labs(
      fill = "Local\nSegregation Index",  # Label for the legend
      title = paste("Local Segregation Index for", state_code),  # Dynamic plot title
      caption = "Data Source: US Census Data"
    )
}

# List of state codes to plot
states_to_plot <- c("OH", "PA", "TN", "WV", "KY", "IN", "IL")

# Generate and print the segregation plots for each state using map from purrr
segregation_plots <- purrr::map(states_to_plot, ~plot_segregation(.x, tracts_segregation_indices))

# Optionally, print the plots
purrr::walk(segregation_plots, print)

Diversity Gradients

The diversity gradient concept uses scatterplot smoothing to illustrate how neighborhood diversity changes with distance or travel time from the core of an urban region (K. Walker 2016b). Traditionally, social science literature on suburbanization suggests that urban cores are more diverse compared to the more segregated and homogeneous suburban neighborhoods. The diversity gradient serves as a visual tool to assess whether this demographic model holds true.

The entropy index for a given geographic unit is calculated as follows:

\[ E = \sum_{r=1}^{n} Q_r \log \frac{1}{Q_r} \]

Where \(Q_r\) represents group \(r\)’s proportion of the population in the geographic unit.

This statistic can be calculated using the entropy() function from the segregation package. Since the entropy() function computes the statistic for one unit at a time, we group the data by tract and use group_modify() to calculate entropy for each tract individually. The argument base = 4 represents the number of groups considered, setting the maximum entropy value to 1, which indicates perfect evenness across the four groups.

# Define the states for analysis
states <- c("OH", "PA", "TN", "WV", "KY", "IN", "IL")

# Initialize an empty list to store data for each state
state_data_list <- vector("list", length(states))
names(state_data_list) <- states

# Loop through each state to get tract data by race/ethnicity
state_data_list <- lapply(states, function(state) {
  get_acs(
    geography = "tract",
    variables = c(
      white = "B03002_003",
      black = "B03002_004",
      asian = "B03002_006",
      hispanic = "B03002_012"
    ),
    state = state,
    geometry = TRUE,
    year = 2019
  )
})

# Combine data from all states
acs_data <- bind_rows(state_data_list)

# Get urbanized areas by population with geometry, filter for populations ≥ 750,000
us_urban_areas <- get_acs(
  geography = "urban area",
  variables = "B01001_001",
  geometry = TRUE,
  year = 2019,
  survey = "acs1"
) %>%
  filter(estimate >= 750000) %>%
  transmute(urban_name = str_remove(NAME, fixed(" Urbanized Area (2010)")))

# Ensure the CRS is consistent between urban areas and tracts
us_urban_areas <- st_transform(us_urban_areas, crs = st_crs(acs_data))

# Compute an inner spatial join between tract data and urbanized areas
urban_data <- acs_data %>%
  st_join(us_urban_areas, left = FALSE) %>%
  select(-NAME) %>%
  st_drop_geometry() %>%
  mutate(dataset = "2015-2019 5-year ACS")

# Function to calculate entropy and plot diversity gradient for a given urban area
analyze_entropy <- function(urban_area_name) {
  # Calculate entropy for each tract in the urban area
  urban_entropy <- urban_data %>%
    filter(urban_name == urban_area_name) %>%
    group_by(GEOID) %>%
    group_modify(~ data.frame(entropy = entropy(
      data = .x,
      group = "variable",
      weight = "estimate",
      base = 4)))
  
  # Get tract geometry and join with entropy data
  urban_entropy_geo <- tracts(state = unique(urban_data$state), cb = TRUE, year = 2019) %>%
    inner_join(urban_entropy, by = "GEOID")
  
  # Calculate the centroid of each tract
  urban_entropy_geo <- urban_entropy_geo %>%
    mutate(centroid = st_centroid(geometry))
  
  # Calculate the centroid of the urban area
  urban_centroid <- us_urban_areas %>%
    filter(urban_name == urban_area_name) %>%
    st_centroid() %>%
    st_geometry()
  
  # Ensure urban_centroid is of the same length as the number of tracts
  urban_centroid <- rep(urban_centroid, nrow(urban_entropy_geo))
  
  # Calculate distance to urban core centroid using tract centroids
  urban_entropy_geo <- urban_entropy_geo %>%
    mutate(distance_to_core = as.numeric(st_distance(centroid, urban_centroid, by_element = TRUE)))
  
# Plot diversity gradient with enhanced visualization
ggplot(urban_entropy_geo %>% na.omit(), aes(x = distance_to_core, y = entropy)) + 
  geom_point(alpha = 0.6, color = "dodgerblue", size = 3) + 
  geom_smooth(method = "loess", level = 0.95, color = "darkred", linetype = "dashed", fill = "lightpink") + 
  theme_minimal() + 
  theme(
    plot.title = element_text(hjust = 0.5, size = 18, face = "bold"),
    axis.title = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 14),
    panel.grid.major = element_line(color = "grey80"),
    panel.grid.minor = element_blank()
  ) + 
  labs(title = paste("Diversity Gradient in", urban_area_name, "(Urbanized Area)"),
       x = "Distance to Urban Core (meters)",
       y = "Entropy Index") +
  geom_rug(sides = "b", color = "darkgrey") 
}

# Analyze and plot diversity gradients for major urban areas
urban_areas_to_analyze <- urban_data$urban_name %>% unique()

lapply(urban_areas_to_analyze, analyze_entropy)
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Regression Modeling with US Census Data

In this analysis, we will be working with US Census data to model regression for variables of interest such as median home value, median income, and several demographic factors. The data is pulled from the American Community Survey (ACS) and visualized through maps and histograms. In this section, we will focus on regression modeling for the state of Pennsylvania (PA). The data is sourced from the 2020 ACS 5-year estimates. We will visualize the data using geographic maps and histograms of median home values.

Data Retrieval

# Get all names for Pennsylvania counties
dfw_counties <- fips_codes %>%
    filter(state == "PA") %>%
    pull(county)

# Define the variables to retrieve from the ACS
variables_to_get <- c(
  median_value = "B25077_001",
  median_rooms = "B25018_001",
  median_income = "DP03_0062",
  total_population = "B01003_001",
  median_age = "B01002_001",
  pct_college = "DP02_0068P",
  pct_foreign_born = "DP02_0094P",
  pct_white = "DP05_0077P",
  median_year_built = "B25037_001",
  percent_ooh = "DP04_0046P"
)

# Get ACS data for Pennsylvania counties
dfw_data <- get_acs(
  geography = "tract",
  variables = variables_to_get,
  state = "PA",
  county = dfw_counties,
  geometry = TRUE,
  output = "wide",
  year = 2020
) %>%
  select(-NAME) 
## Getting data from the 2016-2020 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
# Calculate population density and median structure age
dfw_data_for_model <- dfw_data %>%
  # Calculate population density in people per square kilometer
  mutate(population_density = as.numeric(set_units(total_populationE / st_area(.), "1/km2")),
         median_structure_age = 2018 - median_year_builtE) %>%
  select(!ends_with("M")) %>% 
  rename_with(.fn = ~str_remove(.x, "E$")) %>%
  na.omit()

Regression Modeling

In this section, we are performing a regression modeling analysis to understand the factors influencing median home values in different census tracts. Specifically, we are using a multiple linear regression model where the outcome variable is the natural logarithm of median home value. The natural log transformation is applied to the home values to stabilize variance and normalize the distribution of the values, which is common when dealing with skewed data like home prices. Later in this analysis, the data for median home value will be swapped out with emissions data as the outcome variable to investigate factors affecting emissions across different state however the follwoing analysis is conducted as exploratory.

Regression Equation

We are modeling the log of the median home value (\(\log(\text{median\_value})\)) as a function of several predictors, including:

The model is represented by the following equation:

\[ \log(\text{median\_value}) = \alpha + \beta_{1}(\text{median\_rooms}) + \beta_{2}(\text{median\_income}) + \beta_{3}(\text{pct\_college}) + \beta_{4}(\text{pct\_foreign\_born}) + \beta_{5}(\text{pct\_white}) + \beta_{6}(\text{median\_age}) + \beta_{7}(\text{median\_structure\_age}) + \beta_{8}(\text{percent\_ooh}) + \beta_{9}(\text{pop\_density}) + \beta_{10}(\text{total\_population}) + \epsilon \]

Here, \(\alpha\) is the intercept, and each \(\beta\) represents the coefficient for its respective predictor. The error term (\(\epsilon\)) captures any unexplained variation in the model.

In this section, we fit two linear regression models to examine the relationship between the log of the median home value and various explanatory variables. We present the coefficients, standard errors, t-values, and P-values for each model, along with a Variance Inflation Factor (VIF) analysis to assess multicollinearity.

Model: Full Linear Model

# Define the formula for the linear model
formula <- "log(median_value) ~ median_rooms + median_income + pct_college + pct_foreign_born + pct_white + median_age + median_structure_age + percent_ooh + population_density + total_population"

# Fit the linear model
linear_model <- lm(formula = formula, data = dfw_data_for_model)

# Extract the summary of the linear model
model_summary <- summary(linear_model)

# Convert model coefficients to a dataframe for presentation
model_df <- as.data.frame(coef(model_summary))

# Add meaningful column names
colnames(model_df) <- c("Estimate", "Std. Error", "t value", "P-value")

# Ensure all values are formatted to 2 decimal places including P-values
model_df$Estimate <- round(model_df$Estimate, 3)
model_df$`Std. Error` <- round(model_df$`Std. Error`, 3)
model_df$`t value` <- round(model_df$`t value`, 3)
model_df$`P-value` <- ifelse(model_df$`P-value` < 0.001, 
                             formatC(model_df$`P-value`, format = "e", digits = 3), 
                             round(model_df$`P-value`, 3))

# Create a flextable with model summary
model_flextable <- flextable(model_df) %>%
  set_caption("Linear Model Summary: Coefficients and Significance") %>%
  autofit() %>%
  colformat_num(j = c("Estimate", "Std. Error", "t value"), digits = 3) %>%
  colformat_double(j = "P-value", digits = 3, big.mark = "") %>%
  set_header_labels(
    Estimate = "Estimate",
    `Std. Error` = "Standard Error",
    `t value` = "t-value",
    `P-value` = "P-value"
  )

# Display the flextable
model_flextable
Linear Model Summary: Coefficients and Significance

Estimate

Standard Error

t-value

P-value

10.892

0.056

193.018

0.000e+00

-0.075

0.009

-8.549

1.857e-17

0.000

0.000

25.794

6.345e-134

0.012

0.000

25.596

4.549e-132

0.008

0.001

8.763

2.985e-18

0.002

0.000

7.152

1.050e-12

0.003

0.001

2.925

0.003

0.000

0.000

-10.185

5.174e-24

0.001

0.000

2.595

0.009

0.000

0.000

5.948

2.997e-09

0.000

0.000

11.316

3.709e-29

dfw_estimates <- dfw_data_for_model %>%
  select(-GEOID, -median_value, -median_year_built) %>%
  st_drop_geometry()

# Calculate correlations between variables and present. plot
correlations <- correlate(dfw_estimates, method = "pearson")
network_plot(correlations) 

# Present the VIF table
VIF_table <- vif(linear_model) %>%
  as.data.frame() %>%
  rownames_to_column("Variable") %>%      # Convert rownames (variables) to a column
  mutate(VIF = round(`.` , 3)) %>%        # Round the VIF values to 2 decimal places
  select(Variable, VIF) %>%               # Ensure columns are correctly named
  arrange(desc(VIF)) %>%                  # Sort by VIF
  flextable() %>%
  set_caption("Variance Inflation Factors (VIF)")

VIF_table
Variance Inflation Factors (VIF)

Variable

VIF

median_income

5.075

percent_ooh

3.698

median_rooms

3.218

pct_college

3.085

pct_white

2.532

population_density

2.083

median_age

1.710

pct_foreign_born

1.708

median_structure_age

1.404

total_population

1.180

A VIF value of 1 indicates no collinearity; VIF values above 5 suggest a level of collinearity that has a problematic influence on model interpretation (James et al. 2013). VIF is implemented by the vif() function in the car package (Fox and Weisberg 2019).

Model: Simplified Linear Model

# Define the second formula for the linear model
formula_2 <- "log(median_value) ~ median_rooms + pct_college + pct_foreign_born + pct_white + median_age + median_structure_age + percent_ooh + population_density + total_population"

# Fit the second linear model
linear_model_2 <- lm(formula = formula_2, data = dfw_data_for_model)

# Extract the summary of the second linear model
model_summary_2 <- summary(linear_model_2)

# Convert model coefficients to a dataframe for presentation
model_df_2 <- as.data.frame(coef(model_summary_2))
# Add meaningful column names
colnames(model_df_2) <- c("Estimate", "Std. Error", "t value", "P-value")

# Format P-values in scientific notation where necessary and round other values to 3 digits
model_df_2$Estimate <- round(model_df_2$Estimate, 3)
model_df_2$`Std. Error` <- round(model_df_2$`Std. Error`, 3)
model_df_2$`t value` <- round(model_df_2$`t value`, 3)
model_df_2$`P-value` <- ifelse(model_df_2$`P-value` < 0.001, 
                              formatC(model_df_2$`P-value`, format = "e", digits = 3), 
                              round(model_df_2$`P-value`, 3))

# Create a flextable with model summary
model_flextable_2 <- flextable(model_df_2) %>%
  set_caption("Linear Model Summary: Coefficients and Significance (Model 2)") %>%
  autofit() %>%
  colformat_num(j = c("Estimate", "Std. Error", "t value"), digits = 3) %>%
  colformat_double(j = "P-value", digits = 3, big.mark = "") %>%
  set_header_labels(
    Estimate = "Estimate",
    `Std. Error` = "Standard Error",
    `t value` = "t-value",
    `P-value` = "P-value"
  )

# Display the flextable
model_flextable_2
Linear Model Summary: Coefficients and Significance (Model 2)

Estimate

Standard Error

t-value

P-value

10.425

0.059

178.096

0.000e+00

0.026

0.009

2.992

0.003

0.021

0.000

59.943

0.000e+00

0.011

0.001

11.090

4.307e-28

0.003

0.000

8.542

1.976e-17

0.001

0.001

1.165

0.244

0.000

0.000

-12.134

3.454e-33

0.004

0.001

8.356

9.378e-17

0.000

0.000

4.990

6.362e-07

0.000

0.000

11.495

5.106e-30

# Present the VIF table for the second model
VIF_table_2 <- vif(linear_model_2) %>%
  as.data.frame() %>%
  rownames_to_column("Variable") %>%      # Convert rownames (variables) to a column
  mutate(VIF = round(`.` , 2)) %>%        # Round the VIF values to 2 decimal places
  select(Variable, VIF) %>%               # Ensure columns are correctly named
  arrange(desc(VIF)) %>%                  # Sort by VIF
  flextable() %>%
  set_caption("Variance Inflation Factors (VIF) for Model 2")

# Display the VIF table
VIF_table_2
Variance Inflation Factors (VIF) for Model 2

Variable

VIF

percent_ooh

3.48

median_rooms

2.58

pct_white

2.51

population_density

2.08

median_age

1.70

pct_foreign_born

1.68

pct_college

1.42

median_structure_age

1.39

total_population

1.18

Principle Component Analysis

Principal Component Analysis (PCA) was conducted on the standardized data to reduce dimensionality and explore the relationships among the variables. The PCA summary includes the explained variance of each principal component, formatted into a table. The PCA loadings, which indicate the contribution of each variable to the principal components, were also retrieved. A bar plot of the PCA loadings was created to visualize the relationships between variables and principal components.

# Performing Principal Component Analysis on the standardized data
pca <- prcomp(formula = ~., data = dfw_estimates, scale. = TRUE, center = TRUE)

# Summarizing PCA results
pca_summary <- summary(pca)

# Formatting PCA results into a table for presentation
pca_table <- pca_summary$importance %>%
  as.data.frame() %>%
  rownames_to_column("Metric") %>%
  mutate(Metric = ifelse(Metric == "Comp. 1", "PC1", "PC2")) %>%
  # Rounding only numeric columns to 2 decimal places
  mutate(across(where(is.numeric), ~round(., 2))) %>%
  select(Metric, everything()) %>%
  flextable() %>%
  set_caption("PCA Summary for US Census Data: Explained Variance and Cumulative Proportion")

pca_table
PCA Summary for US Census Data: Explained Variance and Cumulative Proportion

Metric

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

PC10

PC2

1.93

1.41

1.03

0.95

0.83

0.77

0.65

0.56

0.40

0.35

PC2

0.37

0.20

0.11

0.09

0.07

0.06

0.04

0.03

0.02

0.01

PC2

0.37

0.57

0.68

0.77

0.84

0.90

0.94

0.97

0.99

1.00

# Retrieving and formatting PCA loadings
pca_loadings_table <- pca$rotation %>%
  as_tibble(rownames = "predictor") %>%
  mutate(across(where(is.numeric), ~round(., 2))) 

# Displaying PCA loadings
pca_loadings_table %>%
  flextable() %>%
  set_caption("PCA Loadings for US Census Data")
PCA Loadings for US Census Data

predictor

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

PC10

median_rooms

-0.37

0.25

0.09

0.47

0.18

-0.36

0.03

0.10

0.59

-0.24

total_population

-0.08

0.30

0.74

0.09

-0.37

0.41

-0.15

0.16

-0.02

-0.01

median_age

-0.34

-0.21

-0.08

-0.13

0.55

0.61

-0.05

0.35

0.16

0.06

median_income

-0.35

0.46

-0.20

0.02

-0.06

-0.08

0.07

0.05

-0.13

0.77

pct_college

-0.16

0.51

-0.46

-0.24

-0.25

0.16

0.04

0.24

-0.14

-0.53

pct_foreign_born

0.22

0.46

0.10

-0.32

0.46

-0.03

-0.50

-0.39

0.11

-0.03

pct_white

-0.39

-0.21

-0.18

-0.12

-0.41

0.26

-0.15

-0.60

0.38

0.02

percent_ooh

-0.44

0.02

0.14

0.25

0.28

0.03

0.16

-0.43

-0.61

-0.24

population_density

0.36

0.28

-0.03

0.14

0.12

0.38

0.68

-0.29

0.23

0.03

median_structure_age

0.27

0.04

-0.35

0.70

-0.05

0.30

-0.44

-0.02

-0.12

0.04

# Plot the PCA loadings
pca_plot <- pca_loadings_table %>%
  dplyr::select(predictor:PC5) %>% 
  pivot_longer(PC1:PC5, names_to = "component", values_to = "value") %>%
  ggplot(aes(x = value, y = predictor)) + 
  geom_col(fill = "darkolivegreen4", color = "darkolivegreen4", alpha = 0.5) + 
  facet_wrap(~component, nrow = 1) + 
  labs(y = "Variable", x = "Value", title = "US Census Data PCA Loadings Barplot (5-Year ACS, 2015-2019)") +
  theme_minimal() + 
  theme(axis.text.y = element_text(size = 8),
        axis.text.x = element_text(size = 5),
        axis.title = element_text(size = 10),
        strip.text = element_text(size = 10),
        plot.title = element_text(size = 12, hjust = 0.5)
        )

print(pca_plot)

Visualizing PCA Components

The PCA component scores for each observation were calculated and combined with the original GEOID and median home value data. A spatial visualization of the first principal component (PC1) across regions was created to explore spatial patterns in the data. A linear model was then fit using the principal components as predictors to understand the relationship between the principal components and median home values. The results of the PCA-based regression model, including coefficients, standard errors, t-values, and p-values, are presented in the table below.

# Get PCA component scores for each observation
components <- predict(pca, dfw_estimates)

# Create a new dataframe combining the original GEOID and median_value with PCA components
dfw_pca <- dfw_data_for_model %>%
  select(GEOID, median_value) %>%
  cbind(components)

# Visualize the first principal component (PC1) across regions
ggplot(dfw_pca, aes(fill = PC1)) +
  geom_sf(color = NA) +
  theme_void() +
  scale_fill_viridis_c() +
  labs(title = "Spatial Visualization of Principal Component 1 (PC1)")

# Construct a formula for regression using PC1 to PC6 as predictors
pca_formula <- paste0("log(median_value) ~ ", paste0('PC', 1:6, collapse = ' + '))

# Fit a linear model using the principal components as predictors
pca_model <- lm(formula = pca_formula, data = dfw_pca)

# Get a summary of the PCA-based regression model
pca_summary <- summary(pca_model)

# Convert model coefficients to a dataframe for presentation
pca_model_df <- broom::tidy(pca_model) %>%
  mutate(
    estimate = round(estimate, 2),
    std.error = round(std.error, 2),
    statistic = round(statistic, 2),
    p.value = ifelse(p.value < 0.001, format(p.value, scientific = TRUE), round(p.value, 3))
  )

# Create a flextable to present the PCA regression results
pca_model_flextable <- flextable(pca_model_df) %>%
  set_caption("PCA-Based Regression Model Summary: Coefficients and Significance") %>%
  autofit() %>%
  colformat_num(j = c("estimate", "std.error", "statistic"), digits = 2) %>%
  colformat_double(j = "p.value", digits = 2, big.mark = "") %>%
  set_header_labels(
    term = "Predictor",
    estimate = "Estimate",
    std.error = "Standard Error",
    statistic = "t-value",
    p.value = "P-value"
  )

# Display the flextable
pca_model_flextable
PCA-Based Regression Model Summary: Coefficients and Significance

Predictor

Estimate

Standard Error

t-value

P-value

(Intercept)

12.03

0.01

2,345.25

0.000e+00

PC1

-0.14

0.00

-53.51

0.000e+00

PC2

0.27

0.00

74.10

0.000e+00

PC3

-0.11

0.00

-21.34

7.557e-95

PC4

-0.14

0.01

-25.65

1.407e-132

PC5

-0.08

0.01

-13.16

1.272e-38

PC6

0.09

0.01

13.30

2.277e-39

Spatial Regression

Spatial autocorrelation is an important diagnostic when dealing with geographical data. If residuals from a regression model are spatially autocorrelated, the assumption of independent errors in linear regression is violated, potentially leading to biased estimates and incorrect inferences. Moran’s I is commonly used to detect spatial autocorrelation in residuals. If the test shows significant spatial autocorrelation, this suggests that spatial models (such as spatial lag or error models) may be more appropriate than standard OLS models (Anselin 1988).

In this analysis, we check for spatial autocorrelation in the residuals of a simplified linear model using Moran’s I. We also visualize the distribution of residuals and their spatial lagged values to further explore spatial patterns.

# Add residuals to the dataset from the simplified linear model
dfw_data_for_model$residuals <- residuals(linear_model_2)

# Plot histogram of residuals to check their distribution
residuals_histogram <- ggplot(dfw_data_for_model, aes(x = residuals)) + 
  geom_histogram(bins = 100, alpha = 0.7, fill = "darkblue", color = "darkblue") + 
  theme_minimal() +
  labs(title = "Histogram of Residuals", x = "Residuals", y = "Frequency") +
  theme(plot.title = element_text(hjust = 0.5))

# Create spatial weights for neighbors
wts <- dfw_data_for_model %>%
  poly2nb() %>%
  nb2listw()

# Conduct Moran's I test for spatial autocorrelation
moran_test <- moran.test(dfw_data_for_model$residuals, wts)

# Add lagged residuals for spatial lag model visualization
dfw_data_for_model$lagged_residuals <- lag.listw(wts, dfw_data_for_model$residuals)

# Plot residuals vs lagged residuals to visualize spatial dependence
residuals_vs_lagged <- ggplot(dfw_data_for_model, aes(x = residuals, y = lagged_residuals)) + 
  theme_minimal() + 
  geom_point(alpha = 0.6, color = "royalblue3") + 
  geom_smooth(method = "lm", color = "darkblue", se = FALSE) +
  labs(title = "Residuals vs Lagged Residuals", x = "Residuals", y = "Lagged Residuals") +
  theme(plot.title = element_text(hjust = 0.5))

# Display the plots
print(residuals_histogram)

print(residuals_vs_lagged)
## `geom_smooth()` using formula = 'y ~ x'

# Moran's I Test Results formatted as a table
moran_test_flextable <- as.data.frame(t(unclass(moran_test$estimate))) %>%
  setNames(c("Observed Moran's I", "Expected Moran's I", "Variance")) %>%
  rownames_to_column("Metric") %>%
  flextable() %>%
  set_caption("Moran's I Test for Spatial Autocorrelation in Residuals")

# Display Moran's I Test Results
moran_test_flextable
Moran's I Test for Spatial Autocorrelation in Residuals

Metric

Observed Moran's I

Expected Moran's I

Variance

1

0.5265

-0.0002992

0.0001109

In the following section, spatial regression techniques that account for spatial dependence in the data are run. Two primary types of spatial models—spatial lag models and spatial error models—are used to account for spatial autocorrelation.

Spatial Lag Model (Spatial Autocorrelation in Predictors)

The spatial lag model captures spatial dependence by including a spatial lag of the dependent variable (log-transformed median home value) as a predictor. This helps account for the possibility that neighboring regions influence each other. The lagsarlm() function from the spatialreg package is used to estimate this model.

# Define the spatial lag model
lag_model <- lagsarlm(
  formula = formula_2, 
  data = dfw_data_for_model, 
  listw = wts
)

# Summarize the spatial lag model, including Nagelkerke's pseudo R-squared
lag_model_summary <- summary(lag_model, Nagelkerke = TRUE)
print(lag_model_summary)
## 
## Call:lagsarlm(formula = formula_2, data = dfw_data_for_model, listw = wts)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.2293707 -0.1289562  0.0085592  0.1311562  1.1104693 
## 
## Type: lag 
## Coefficients: (asymptotic standard errors) 
##                           Estimate    Std. Error z value              Pr(>|z|)
## (Intercept)           3.4476056535  0.1266919726 27.2125 < 0.00000000000000022
## median_rooms          0.0202824178  0.0059264169  3.4224             0.0006208
## pct_college           0.0104183118  0.0003193911 32.6193 < 0.00000000000000022
## pct_foreign_born      0.0036275336  0.0006677688  5.4323   0.00000005562619387
## pct_white             0.0009922264  0.0002351484  4.2196   0.00002447636080216
## median_age            0.0020736287  0.0006548608  3.1665             0.0015428
## median_structure_age -0.0000519025  0.0000062208 -8.3434 < 0.00000000000000022
## percent_ooh           0.0026979842  0.0003493322  7.7233   0.00000000000001132
## population_density    0.0000077125  0.0000017722  4.3519   0.00001349705341958
## total_population      0.0000260699  0.0000027635  9.4338 < 0.00000000000000022
## 
## Rho: 0.6361, LR test value: 2232, p-value: < 0.000000000000000222
## Asymptotic standard error: 0.01124
##     z-value: 56.62, p-value: < 0.000000000000000222
## Wald statistic: 3206, p-value: < 0.000000000000000222
## 
## Log likelihood: 199.4 for lag model
## ML residual variance (sigma squared): 0.04757, (sigma: 0.2181)
## Nagelkerke pseudo-R-squared: 0.85 
## Number of observations: 3343 
## Number of parameters estimated: 12 
## AIC: -374.8, (AIC for lm: 1855)
## LM test for residual autocorrelation
## test value: 21.7, p-value: 0.0000031904
# Running Morans Test on the residuals
moran.test(lag_model$residuals, wts)
## 
##  Moran I test under randomisation
## 
## data:  lag_model$residuals  
## weights: wts    
## 
## Moran I statistic standard deviate = 3.7, p-value = 0.0001
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic       Expectation          Variance 
##         0.0388857        -0.0002992         0.0001108

Spatial Error Model (Spatial Autocorrelation in Errors)

The spatial error model captures spatial dependence in the error terms, rather than in the predictors. It is useful when spatial correlation affects the unexplained variation in the model. The errorsarlm() function is used to estimate this model.

# Define the spatial error model
error_model <- errorsarlm(
  formula = formula_2, 
  data = dfw_data_for_model, 
  listw = wts
)

# Summarize the spatial error model, including Nagelkerke's pseudo R-squared
error_model_summary <- summary(error_model, Nagelkerke = TRUE)

# Print the summary of the spatial error model
error_model_summary
## 
## Call:errorsarlm(formula = formula_2, data = dfw_data_for_model, listw = wts)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -1.2921154 -0.1142056  0.0055431  0.1182663  1.0262763 
## 
## Type: error 
## Coefficients: (asymptotic standard errors) 
##                           Estimate    Std. Error  z value              Pr(>|z|)
## (Intercept)          10.7016488225  0.0518831521 206.2644 < 0.00000000000000022
## median_rooms          0.0405803540  0.0064137179   6.3271  0.000000000249781307
## pct_college           0.0142530485  0.0004148602  34.3563 < 0.00000000000000022
## pct_foreign_born      0.0043285243  0.0008621995   5.0203  0.000000515833533887
## pct_white             0.0038609661  0.0003866788   9.9849 < 0.00000000000000022
## median_age            0.0029114849  0.0006252446   4.6566  0.000003215471263696
## median_structure_age -0.0000554951  0.0000060287  -9.2051 < 0.00000000000000022
## percent_ooh           0.0014256720  0.0003545377   4.0212  0.000057899072719581
## population_density   -0.0000105136  0.0000024748  -4.2482  0.000021546802292161
## total_population      0.0000204707  0.0000026114   7.8389  0.000000000000004441
## 
## Lambda: 0.8525, LR test value: 2377, p-value: < 0.000000000000000222
## Asymptotic standard error: 0.01018
##     z-value: 83.75, p-value: < 0.000000000000000222
## Wald statistic: 7014, p-value: < 0.000000000000000222
## 
## Log likelihood: 271.9 for error model
## ML residual variance (sigma squared): 0.04105, (sigma: 0.2026)
## Nagelkerke pseudo-R-squared: 0.8563 
## Number of observations: 3343 
## Number of parameters estimated: 12 
## AIC: -519.7, (AIC for lm: 1855)
# Running Morans Test on the error model 
moran.test(error_model$residuals, wts)
## 
##  Moran I test under randomisation
## 
## data:  error_model$residuals  
## weights: wts    
## 
## Moran I statistic standard deviate = -10, p-value = 1
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic       Expectation          Variance 
##        -0.1081375        -0.0002992         0.0001108

Spatial Dependence Test

The Lagrange Multiplier (LM) tests help detect the presence of spatial dependence in the residuals of an ordinary least squares (OLS) model. These tests help determine whether spatial error or spatial lag models are necessary.

# Perform Lagrange Multiplier (LM) tests for spatial dependence in residuals
lm_tests <- lm.LMtests(
  model = linear_model_2,  # OLS model
  wts, # Spatial weights
  test = c("LMerr", "LMlag", "RLMerr", "RLMlag")  # Testing for spatial error and lag dependence
)

# Print the results of the LM tests
lm_tests
## 
##  Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
##  dependence
## 
## data:  
## model: lm(formula = formula_2, data = dfw_data_for_model)
## test weights: listw
## 
## RSerr = 2496, df = 1, p-value <0.0000000000000002
## 
## 
##  Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
##  dependence
## 
## data:  
## model: lm(formula = formula_2, data = dfw_data_for_model)
## test weights: listw
## 
## RSlag = 2371, df = 1, p-value <0.0000000000000002
## 
## 
##  Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
##  dependence
## 
## data:  
## model: lm(formula = formula_2, data = dfw_data_for_model)
## test weights: listw
## 
## adjRSerr = 529, df = 1, p-value <0.0000000000000002
## 
## 
##  Rao's score (a.k.a Lagrange multiplier) diagnostics for spatial
##  dependence
## 
## data:  
## model: lm(formula = formula_2, data = dfw_data_for_model)
## test weights: listw
## 
## adjRSlag = 403, df = 1, p-value <0.0000000000000002

Geographically Weighted Regression

The models addressed in the previous sections, including both the regular linear model and its spatial adaptations, estimate global relationships between the outcome variable (e.g., median home values) and its predictors. However, relationships between a predictor and the outcome variable observed for the entire region may vary significantly from neighborhood to neighborhood. This phenomenon is called spatial non-stationarity, and it can be explored using Geographically Weighted Regression (GWR).

Geographically Weighted Regression (GWR) is a spatial regression technique that allows the relationship between the dependent variable and independent variables to vary across space. GWR estimates local regression coefficients for each observation, providing insights into spatially varying relationships that may be masked in global regression models. In this analysis, we fit a GWR model to explore the spatially varying relationships between median home values and selected predictors.

GWR evaluates local variations in regression models using a kernel-based weighting function. The basic form of GWR for a given location \(i\) is:

\[ Y_i = \alpha_i + \sum\limits_{k=1}^m \beta_{ik} X_{ik} + \epsilon_i \]

where: - \(Y_i\) is the outcome at location \(i\), - \(\alpha_i\) is the location-specific intercept, - \(\beta_{ik}\) is the local regression coefficient for predictor \(k\), - \(\epsilon_i\) is the error term at location \(i\).

GWR uses a kernel bandwidth to compute the local regression model for each location. This bandwidth can be either fixed (based on a distance cutoff) or adaptive (using nearest neighbors). In our case, using Census tract data where the size of tracts varies widely, an adaptive kernel is preferred.

Fitting and Evaluating the GWR Model

The code below shows how to fit a GWR model with a defined formula and plot the maps of local R-squared values. Furthermore local parameter estimates for the percentage of owner-occupied housing, and local parameter estimates for population density are also included in order to provide insights into the spatially varying relationships between the predictors and median home values.

# Convert data to spatial format for GWmodel compatibility
dfw_data_sp <- dfw_data_for_model %>%
  as_Spatial()

# Choose the bandwidth using cross-validation
bw <- bw.gwr(
  formula = formula_2, 
  data = dfw_data_sp, 
  kernel = "bisquare",
  adaptive = TRUE
)
## Take a cup of tea and have a break, it will take a few minutes.
##           -----A kind suggestion from GWmodel development group
## Adaptive bandwidth: 2073 CV score: 222.6 
## Adaptive bandwidth: 1289 CV score: 200.7 
## Adaptive bandwidth: 803 CV score: 185 
## Adaptive bandwidth: 504 CV score: 166.6 
## Adaptive bandwidth: 318 CV score: 155.3 
## Adaptive bandwidth: 204 CV score: 149.2 
## Adaptive bandwidth: 132 CV score: 147.2 
## Adaptive bandwidth: 89 CV score: 225.5 
## Adaptive bandwidth: 160 CV score: 147.3 
## Adaptive bandwidth: 116 CV score: 147.2 
## Adaptive bandwidth: 104 CV score: 147.9 
## Adaptive bandwidth: 121 CV score: 147.1 
## Adaptive bandwidth: 126 CV score: 147.2 
## Adaptive bandwidth: 119 CV score: 147.1 
## Adaptive bandwidth: 123 CV score: 147.1 
## Adaptive bandwidth: 120 CV score: 147.1 
## Adaptive bandwidth: 122 CV score: 147.1 
## Adaptive bandwidth: 122 CV score: 147.1
# Fit the GWR model
gw_model <- gwr.basic(
  formula = formula_2, 
  data = dfw_data_sp, 
  bw = bw,
  kernel = "bisquare",
  adaptive = TRUE
)

# Print the model results
gw_model_results <- gw_model$SDF %>%
  st_as_sf() 

# Plot the GWR results
ggplot(gw_model_results, aes(fill = Local_R2)) + 
  geom_sf(color = NA) + 
  scale_fill_viridis_c() + 
  theme_void()  + 
  labs(title = "Local R-Squared Values from the GWR Model", fill = expression("Local R"^2)) + 
  theme(plot.title = element_text(hjust = 0.5))

# Plot the local relationships between the percentage owner-occupied housing and median home values
ggplot(gw_model_results, aes(fill = percent_ooh)) + 
  geom_sf(color = NA) + 
  scale_fill_viridis_c() + 
  theme_void() + 
  labs(title = "Local β for the GWR Model (% Owner-Occupied Housing)",
         fill = "Local β for \npercentage owner-occupied housing ")+ 
  theme(plot.title = element_text(hjust = 0.5))

# Explore this further by investigating the local parameter estimates for population density
ggplot(gw_model_results, aes(fill = population_density)) + 
  geom_sf(color = NA) + 
  scale_fill_viridis_c() + 
  theme_void() + 
  labs(title = "Local β for the GWR Model (Population Density)",
       fill = "Local β for \npopulation density") + 
  theme(plot.title = element_text(hjust = 0.5))

Classification and Clustering

The statistical models discussed earlier were used to understand relationships between an outcome variable and a series of predictors. In the context of emissions analysis, these models are critical for identifying and understanding the factors contributing to environmental outcomes like air pollution or greenhouse gas emissions. Geodemographic clustering groups areas based on similarities in demographic and environmental factors, which can also be applied to emissions data. For example, regions with low emissions might cluster together, whereas areas with predominantly industrial emissions might form another cluster. Regionalization creates contiguous areas based on similarities in values, demographic factors, or other key indicators. This is particularly useful for spatial analysis, where regions with similar environmental/demographic trends can be grouped together. Below is an example using dimension reduction (principal component analysis) followed by k-means clustering to identify distinct geodemographic groups within the area of interest.

K-Means Clustering

We use the k-means clustering algorithm to partition the dataset into distinct groups. The k-means algorithm attempts to generate k clusters that are internally similar but dissimilar from other clusters. In R, the k-means clustering can be implemented with the kmeans() function. Below is an example that demonstrates how we can use k-means to classify regions based on principal component analysis (PCA) results. This helps to reduce the dimensionality of the data while maintaining important patterns.

# Setting seed for reproducibility
set.seed(123)

# Calculate statistical validation of the number of clusters (gap statistic)
#gap_stat <- clusGap(dfw_pca %>% st_drop_geometry() %>% select(PC1:PC8),
                   # FUN = kmeans, nstart = 25, K.max = 10, B = 50)

# Plot the gap statistic
#fviz_gap_stat(gap_stat) # 7 clusters seems to be the optimal number

# Perform k-means clustering on the PCA-transformed data (assume dfw_pca is created)
dfw_kmeans <- dfw_pca %>%
  st_drop_geometry() %>%
  select(PC1:PC8) %>%
  kmeans(centers = 7)

# Assign the cluster IDs to the original dataset
dfw_clusters <- dfw_pca %>%
  mutate(cluster = as.character(dfw_kmeans$cluster))

# Visualize the clusters on a map
ggplot(dfw_clusters, aes(fill = cluster)) + 
  geom_sf(size = 0.1) + 
  scale_fill_brewer(palette = "Set1") + 
  theme_void() + 
  labs(fill = "Cluster", title = "Geodemographic Clusters (K-Means Clustering)") +
  theme(plot.title = element_text(hjust = 0.5))

# Visualize the clusters on a plot using ggplotly for interactivity
cluster_plot <- ggplot(dfw_clusters, 
                       aes(x = PC1, y = PC2, color = cluster)) + 
  geom_point() + 
  scale_color_brewer(palette = "Set1") + 
  theme_minimal() 

ggplotly(cluster_plot) %>%
  layout(legend = list(orientation = "h", y = -0.15, 
                       x = 0.2, title = "Cluster")) %>%
  config(displayModeBar = F) %>% 
  layout(title = "Geodemographic Clusters (K-Means Clustering)")
# Create a data frame with the count of regions per cluster
cluster_counts <- as.data.frame(table(dfw_kmeans$cluster))
# Rename the columns for better readability
colnames(cluster_counts) <- c("Cluster", "Number of Regions")
# Create a flextable to display the cluster counts
cluster_flextable <- flextable(cluster_counts) %>%
  set_caption("Number of Regions per Cluster") %>%
  autofit()

# Display the table
cluster_flextable
Number of Regions per Cluster

Cluster

Number of Regions

1

878

2

108

3

435

4

262

5

331

6

820

7

509

Spatial Clustering

Spatial clustering is a technique used to identify spatial patterns in data, grouping regions with similar characteristics together. In the context of emissions analysis, spatial clustering can help identify areas with similar emission profiles, which can be useful for targeted policy interventions or resource allocation. In this section, we demonstrate how to perform spatial clustering using the SKATER algorithm (Assunção et al. 2006). This algorithm is implemented in R with the skater() function in the spdep package and is also available in PySAL, GeoDa, and ArcGIS as the “Spatially Constrained Multivariate Clustering” tool.

# Prepare the input data for the SKATER algorithm by selecting the first 8 principal components
input_vars <- dfw_pca %>%
  select(PC1:PC8) %>%      # Select principal components (PC1 to PC8) from the PCA-transformed data
  st_drop_geometry() %>%    # Remove spatial (geometry) information to focus on numerical data
  as.data.frame()        

# Create a neighborhood structure (adjacency list) using Queen contiguity (neighboring polygons share an edge)
skater_nbrs <- poly2nb(dfw_pca, queen = TRUE)

# Calculate the "costs" (differences) between neighboring polygons based on the input variables
costs <- nbcosts(skater_nbrs, input_vars)

# Create a spatial weights object using the neighbor costs, with "B" style to set binary weights
skater_weights <- nb2listw(skater_nbrs, costs, style = "B")

# Generate a minimum spanning tree (MST) from the spatial weights object
mst <- mstree(skater_weights)

# Perform the SKATER algorithm to generate 7 regional clusters (using ncuts = 7), ensuring each region has at least 10 Census tracts (crit = 10)
#regions <- skater(
 # mst[,1:2],  # The first two columns of the MST (the edges of the tree)
 # input_vars, # The input data (principal components)
 # ncuts = 5,  # Number of cuts (creates 9 regions)
 # crit = 10   # Minimum number of tracts per region
 # )

# Assign the resulting region labels (clusters) to the original spatial data frame
 # dfw_clusters$region <- as.character(regions$group)

# Visualize the resulting regions on a map using ggplot2
# ggplot(dfw_clusters, aes(fill = region)) + 
 # geom_sf(size = 0.1) +       
 # scale_fill_brewer(palette = "Set1")
 # theme_void()                     

Foor Loop for Downloading ACS Data

For the purpose of this analysis, we will download the American Community Survey (ACS) data for selected states and years. The ACS provides detailed demographic, social, economic, and housing data for the United States, making it a valuable resource for understanding regional characteristics. We will focus on the following states: Ohio (OH), Pennsylvania (PA), Tennessee (TN), West Virginia (WV), Kentucky (KY), Indiana (IN), and Illinois (IL). The data will be downloaded for the years 2005, 2010, and 2015 to capture changes over time.

Downloading data

# List of states for which to retrieve data
states <- c("OH", "PA", "TN", "WV", "KY", "IN", "IL")

# Variables to retrieve from the ACS
variables_to_get <- c(
  # General wealth and housing indicators
  median_value = "B25077_001",          # Median home value
  median_rooms = "B25018_001",          # Median number of rooms
  median_income = "DP03_0062",          # Median household income
  total_population = "B01003_001",      # Total population
  median_age = "B01002_001",            # Median age
  pct_college = "DP02_0068P",           # Percent with Bachelor's degree or higher
  pct_foreign_born = "DP02_0094P",      # Percent foreign-born population
  pct_white = "DP05_0077P",             # Percent White population
  median_year_built = "B25037_001",     # Median year structure built
  percent_ooh = "DP04_0046P",           # Percent owner-occupied housing

  # Income by race
  median_income_black = "B19013B_001",  # Median household income for Black or African American households
  median_income_white = "B19013H_001",  # Median household income for White (non-Hispanic) households
  median_income_hispanic = "B19013I_001",# Median household income for Hispanic or Latino households
  median_income_asian = "B19013D_001",  # Median household income for Asian households
  
  # Per capita income by race
  per_capita_income_black = "B19301B_001",  # Per capita income for Black or African American households
  per_capita_income_white = "B19301H_001",  # Per capita income for White (non-Hispanic) households
  per_capita_income_hispanic = "B19301I_001",# Per capita income for Hispanic or Latino households
  per_capita_income_asian = "B19301D_001"  # Per capita income for Asian households

)

# Function to list all counties for a given state
list_counties_by_state <- function(state_abbreviation) {
  fips_codes %>%
    filter(state == state_abbreviation) %>%
    pull(county)
}

# Years for which to retrieve ACS data (5-year estimates)
years <- seq(2009, 2020, by = 5)

# Initialize an empty list to store the data for each state and year
all_data <- list()

# Loop through each year, then through each state, and retrieve the ACS data
for (year in years) {
  
  # Initialize a list to store data for this year
  year_data_list <- list()
  
  for (state in states) {
    
    # List all counties in the current state
    state_counties <- list_counties_by_state(state)
    
    # Retrieve ACS data for the state's counties
    state_data <- get_acs(
      geography = "tract",
      variables = variables_to_get,
      state = state,
      county = state_counties,
      geometry = TRUE,
      output = "wide",
      year = year
    ) %>%
      select(-NAME) %>%               # Remove the NAME column
      st_transform(crs = 5070) %>%    # NAD83 / Conus Albers projection
      mutate(dataset = paste0(year - 4, "-", year, " 5-year ACS"),
             state = state)           # Add a dataset and state column
    
    # Append the state's data for this year to the list
    year_data_list[[state]] <- state_data
  }
  
  # Combine the data for all states for this year
  combined_year_data <- bind_rows(year_data_list)
  
  # Append this year's data to the master list
  all_data[[as.character(year)]] <- combined_year_data
}
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2005-2009 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2010-2014 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
## Getting data from the 2015-2019 5-year ACS
## Fetching data by table type ("B/C", "S", "DP") and combining the result.
# Combine the data from all years into a single data frame
final_combined_data <- bind_rows(all_data)

# Ensure the geometry column is dropped correctly using st_drop_geometry()
flextable(
  final_combined_data %>%
    st_drop_geometry() %>%               # Correctly remove the geometry column
    head(15)                          # Select the first 15 rows
) %>%
  set_caption("Census Tract-Level Data (Selected Variables), 5-year ACS") %>%
  autofit() %>%
  colformat_num(j = c("median_valueE", "median_valueM", "median_roomsE", "median_roomsM",
                      "median_incomeE", "median_incomeM", "total_populationE", "total_populationM",
                      "median_ageE", "median_ageM", "median_year_builtE", "median_year_builtM",
                      "pct_collegeE", "pct_collegeM", "pct_whiteE", "pct_whiteM",
                      "pct_foreign_bornE", "pct_foreign_bornM", "percent_oohE", "percent_oohM"),
                digits = 2) %>%  # Format numerical columns
  set_header_labels(
    GEOID = "Census Tract GEOID",
    median_valueE = "Median Home Value Estimate",
    median_valueM = "Median Home Value Margin of Error",
    median_roomsE = "Median Rooms Estimate",
    median_roomsM = "Median Rooms Margin of Error",
    median_incomeE = "Median Income Estimate",
    median_incomeM = "Median Income Margin of Error",
    total_populationE = "Total Population Estimate",
    total_populationM = "Total Population Margin of Error",
    median_ageE = "Median Age Estimate",
    median_ageM = "Median Age Margin of Error",
    median_year_builtE = "Median Year Built Estimate",
    median_year_builtM = "Median Year Built Margin of Error",
    pct_collegeE = "Percent College-Educated Estimate",
    pct_collegeM = "Percent College-Educated Margin of Error",
    pct_whiteE = "Percent White Estimate",
    pct_whiteM = "Percent White Margin of Error",
    pct_foreign_bornE = "Percent Foreign-Born Estimate",
    pct_foreign_bornM = "Percent Foreign-Born Margin of Error",
    percent_oohE = "Percent Owner-Occupied Housing Estimate",
    percent_oohM = "Percent Owner-Occupied Housing Margin of Error",
    state = "State",
    dataset = "Dataset"
  )
Census Tract-Level Data (Selected Variables), 5-year ACS

Census Tract GEOID

Median Home Value Estimate

Median Home Value Margin of Error

Median Rooms Estimate

Median Rooms Margin of Error

Total Population Estimate

Total Population Margin of Error

Median Age Estimate

Median Age Margin of Error

Median Year Built Estimate

Median Year Built Margin of Error

median_income_blackE

median_income_blackM

median_income_whiteE

median_income_whiteM

median_income_hispanicE

median_income_hispanicM

median_income_asianE

median_income_asianM

per_capita_income_blackE

per_capita_income_blackM

per_capita_income_whiteE

per_capita_income_whiteM

per_capita_income_hispanicE

per_capita_income_hispanicM

per_capita_income_asianE

per_capita_income_asianM

Median Income Estimate

Median Income Margin of Error

Percent College-Educated Estimate

Percent College-Educated Margin of Error

Percent Foreign-Born Estimate

Percent Foreign-Born Margin of Error

Percent White Estimate

Percent White Margin of Error

Percent Owner-Occupied Housing Estimate

Percent Owner-Occupied Housing Margin of Error

Dataset

State

39001990100

97,100

8,604

5.4

0.3

5,250

432

38.1

2.2

1,978

4

0

32,357

5,028

17,083

31,150

0

1,830

2,904

20,925

5,866

28,800

16,466

41

46

3,977

0

23.1

41.4

0.0

0.6

32.7

5.8

2005-2009 5-year ACS

OH

39001990200

96,100

5,683

5.6

0.2

4,615

343

38.4

3.9

1,974

3

0

35,318

3,539

0

0

19,435

2,964

36

41

3,317

0

100.0

57.1

0.0

0.7

28.1

4.9

2005-2009 5-year ACS

OH

39001990300

115,800

16,756

5.7

0.3

7,185

433

37.1

2.1

1,985

4

0

42,267

5,582

39,375

46,819

0

17,616

1,941

14,095

6,408

28

44

5,177

0

100.0

66.3

0.0

0.5

16.0

4.4

2005-2009 5-year ACS

OH

39001990400

93,100

5,898

5.3

0.3

4,492

435

40.5

8.7

1,974

4

0

31,987

7,082

31,667

3,397

0

17,968

1,993

10,654

3,707

0

119

3,526

0

0.0

37.0

0.0

0.7

32.3

7.4

2005-2009 5-year ACS

OH

39001990500

82,700

10,020

5.2

0.2

3,328

399

37.5

2.3

1,984

4

0

31,515

3,269

0

0

15,293

1,975

0

119

2,558

0

666,666,666.0

0.0

0.0

1.0

29.0

6.6

2005-2009 5-year ACS

OH

39001990600

76,200

10,572

5.1

0.3

3,272

327

41.0

3.0

1,973

3

0

26,403

4,853

0

0

0

14,550

1,577

7,835

2,695

10

15

2,542

0

100.0

100.0

0.0

1.0

29.8

5.6

2005-2009 5-year ACS

OH

39003010100

134,400

13,332

6.4

0.3

4,346

207

31.2

6.2

1,963

6

0

59,412

7,928

0

0

6,681

5,125

24,353

3,044

15,556

12,965

32

27

3,538

0

29.3

25.6

0.0

0.7

25.1

5.5

2005-2009 5-year ACS

OH

39003010200

120,700

13,223

6.6

0.2

3,910

246

40.4

2.0

1,965

6

0

55,852

5,047

0

0

4,539

4,505

25,794

1,941

31,388

16,609

26

19

2,926

0

0.0

61.8

0.0

0.8

9.8

4.1

2005-2009 5-year ACS

OH

39003010300

151,200

26,070

6.5

0.5

1,617

118

40.5

2.5

1,967

15

0

52,198

5,907

0

0

22,399

2,405

0

119

1,160

0

666,666,666.0

0.0

0.0

2.0

14.0

7.9

2005-2009 5-year ACS

OH

39003010600

98,500

5,583

6.3

0.3

4,923

200

39.9

2.4

1,960

5

0

51,397

4,934

4,375

169,966

0

23,913

1,977

15,279

20,075

38

29

3,732

0

51.4

41.1

0.8

1.3

12.4

3.8

2005-2009 5-year ACS

OH

39003010800

136,600

10,135

6.3

0.2

8,489

385

40.7

2.4

1,975

3

41,250

53,273

62,123

5,063

37,109

85,541

0

17,038

5,962

29,726

2,154

60,396

44,997

44,266

23,549

154

68

6,315

0

48.2

31.9

0.4

0.6

17.1

3.8

2005-2009 5-year ACS

OH

39003010900

126,300

9,740

5.9

0.4

4,793

747

34.6

8.5

1,970

3

41,439

20,637

49,639

5,290

12,404

29,985

0

21,468

4,235

26,084

3,788

6,712

3,526

38

28

3,900

0

100.0

36.3

0.0

0.7

24.8

5.4

2005-2009 5-year ACS

OH

39003011000

96,700

11,326

5.0

0.4

5,429

593

32.3

3.6

1,977

3

24,042

20,562

36,869

3,885

102,500

44,650

0

13,625

6,532

20,485

2,248

14,209

10,386

10,463

12,275

0

119

4,284

0

100.0

22.0

1.4

2.0

52.5

6.3

2005-2009 5-year ACS

OH

39003011200

73,300

13,396

5.1

0.7

2,585

229

40.1

3.4

1,954

8

0

38,833

6,115

0

0

1,073

1,467

8,957

1,559

3,491

2,941

0

119

2,228

0

64.7

39.4

0.0

1.2

32.8

14.0

2005-2009 5-year ACS

OH

39003011300

135,900

6,753

6.1

0.2

6,699

243

40.2

3.4

1,972

2

23,750

29,122

58,310

5,072

0

0

10,340

5,206

25,967

2,130

40,985

41,488

24

27

5,029

0

84.1

28.9

0.0

0.5

15.0

4.7

2005-2009 5-year ACS

OH

# Write out the data to a CSV file
write_csv(final_combined_data, here::here("Output", "US_Census_Tract_Data_Population_2005_2020.csv"))
acs_data_states_of_interest <- read_csv(here::here("Output", "US_Census_Tract_Data_Population_2005_2020.csv"))

Regression Modeling Example

Multiple States, Household Value

# List of variables to retrieve from the ACS
variables_to_get <- c(
  median_value = "B25077_001",
  median_rooms = "B25018_001",
  median_income = "DP03_0062",
  total_population = "B01003_001",
  median_age = "B01002_001",
  pct_college = "DP02_0068P",
  pct_foreign_born = "DP02_0094P",
  pct_white = "DP05_0077P",
  median_year_built = "B25037_001",
  percent_ooh = "DP04_0046P"
)

# Function to create maps and histograms for a state
create_maps_and_histograms <- function(state_abbreviation) {
  # List all counties for the state
  dfw_counties <- fips_codes %>%
    filter(state == state_abbreviation) %>%
    pull(county)
  
  # Retrieve ACS data for the state
  dfw_data <- get_acs(
    geography = "tract", # get data at the tract level
    variables = variables_to_get,
    state = state_abbreviation,
    county = dfw_counties,
    geometry = TRUE,
    output = "wide",
    year = 2020
  ) %>%
    select(-NAME) %>%
    filter(!is.na(median_valueE))  # Remove missing values
  
  # Create Median Home Value Map
  mhv_map <- ggplot(dfw_data, aes(fill = median_valueE)) + 
    geom_sf(color = NA) + 
    scale_fill_viridis_c(labels = scales::label_dollar()) + 
    theme_void() + 
    labs(fill = "Median Home Value ($)", title = paste("Median Home Value Map -", state_abbreviation)) +
    theme(axis.text=element_text(size=6),
          plot.title = element_text(size=10),
          axis.title = element_text(size=8),
          legend.title = element_text(size = 8))
  
  # Create Median Home Value Histogram
  mhv_histogram <- ggplot(dfw_data, aes(x = median_valueE)) + 
    geom_histogram(alpha = 0.7, fill = "navy", color = "navy", bins = 100) + 
    theme_minimal() + 
    scale_x_continuous(labels = scales::label_dollar()) + 
    labs(x = "Median Home Value ($)", y = "Count", title = paste("Median Home Value Distribution -", state_abbreviation)) +
    theme(axis.text=element_text(size=6),
          plot.title = element_text(size=10),
          axis.title = element_text(size=8),
          legend.title = element_text(size = 8))
  
  # Create Log-transformed Median Home Value Map
  mhv_map_log <- ggplot(dfw_data, aes(fill = log(median_valueE))) + 
    geom_sf(color = NA) + 
    scale_fill_viridis_c() + 
    theme_void() + 
    labs(fill = "Log(Median Home Value)", title = paste("Log Median Home Value Map -", state_abbreviation))+
    theme(axis.text=element_text(size=6),
          plot.title = element_text(size=10),
          axis.title = element_text(size=8),
          legend.title = element_text(size = 8))
  
  # Create Log-transformed Median Home Value Histogram
  mhv_histogram_log <- ggplot(dfw_data, aes(x = log(median_valueE))) + 
    geom_histogram(alpha = 0.7, fill = "navy", color = "navy", bins = 100) + 
    theme_minimal() + 
    scale_x_continuous() + 
    labs(x = "Log(Median Home Value)", y = "Count", title = paste("Log Median Home Value Distribution -", state_abbreviation)) +
    theme(axis.text=element_text(size=6),
          plot.title = element_text(size=10),
          axis.title = element_text(size=8),
          legend.title = element_text(size = 8))
  
  # Display all plots side-by-side
  combined_plot <- (mhv_map + mhv_histogram) / (mhv_map_log + mhv_histogram_log)
  
  # Print the combined plot to display
  print(combined_plot)
}

# List of states to generate data and figures for
create_maps_and_histograms("PA")

create_maps_and_histograms("OH")

create_maps_and_histograms("TN")

create_maps_and_histograms("WV")

create_maps_and_histograms("KY")

create_maps_and_histograms("IN")

create_maps_and_histograms("IL")

Multiple States, Per Capita Income

# List of variables to retrieve from the ACS
variables_to_get <- c(
  per_capita_income_black = "B19301B_001",   # Per capita income for Black population
  per_capita_income_white = "B19301H_001",   # Per capita income for White (non-Hispanic) population
  per_capita_income_hispanic = "B19301I_001",# Per capita income for Hispanic population
  per_capita_income_asian = "B19301D_001"    # Per capita income for Asian population
)

# Function to create maps and histograms for income by race for a state
create_income_maps_and_histograms <- function(state_abbreviation) {
  # List all counties for the state
  state_counties <- fips_codes %>%
    filter(state == state_abbreviation) %>%
    pull(county)
  
  # Retrieve ACS data for the state
  income_data <- get_acs(
    geography = "tract", # get data at the tract level
    variables = variables_to_get,
    state = state_abbreviation,
    county = state_counties,
    geometry = TRUE,
    output = "wide",
    year = 2020
  ) %>%
    select(-NAME)  # Remove the name column
  
  # Create function to generate plots for each race, with filtering for NA values
  create_plots_for_race <- function(race_col, race_label, color, log_color) {
    # Filter out NAs for the specific race
    income_data_race <- income_data %>%
      filter(!is.na(!!sym(race_col)))
    
    # Create Per Capita Income Map for the race
    income_map <- ggplot(income_data_race, aes(fill = !!sym(race_col))) + 
      geom_sf(color = NA) + 
      scale_fill_viridis_c(labels = scales::label_dollar()) + 
      theme_void() + 
      labs(fill = "Per Capita Income ($)", title = paste("Per Capita Income (", race_label, ") -", state_abbreviation)) +
      theme(axis.text=element_text(size=6),
            plot.title = element_text(size=10),
            axis.title = element_text(size=8),
            legend.title = element_text(size = 8))
    
    # Create Per Capita Income Histogram for the race
    income_histogram <- ggplot(income_data_race, aes(x = !!sym(race_col))) + 
      geom_histogram(alpha = 0.7, fill = color, color = color, bins = 100) + 
      theme_minimal() + 
      scale_x_continuous(labels = scales::label_dollar()) + 
      labs(x = "Per Capita Income ($)", y = "Count", title = paste("Per Capita Income Distribution (", race_label, ") -", state_abbreviation)) +
      theme(axis.text=element_text(size=6),
            plot.title = element_text(size=10),
            axis.title = element_text(size=8),
            legend.title = element_text(size = 8))
    
    # Log-transformed map for the race
    income_map_log <- ggplot(income_data_race, aes(fill = log(!!sym(race_col)))) + 
      geom_sf(color = NA) + 
      scale_fill_viridis_c() + 
      theme_void() + 
      labs(fill = "Log(Per Capita Income)", title = paste("Log Per Capita Income (", race_label, ") -", state_abbreviation)) +
      theme(axis.text=element_text(size=6),
            plot.title = element_text(size=10),
            axis.title = element_text(size=8),
            legend.title = element_text(size = 8))
    
    # Log-transformed histogram for the race
    income_histogram_log <- ggplot(income_data_race, aes(x = log(!!sym(race_col)))) + 
      geom_histogram(alpha = 0.7, fill = log_color, color = log_color, bins = 100) + 
      theme_minimal() + 
      scale_x_continuous() + 
      labs(x = "Log(Per Capita Income)", y = "Count", title = paste("Log Per Capita Income Distribution (", race_label, ") -", state_abbreviation)) +
      theme(axis.text=element_text(size=6),
            plot.title = element_text(size=10),
            axis.title = element_text(size=8),
            legend.title = element_text(size = 8))
    
    # Combine the map and histogram for both original and log-transformed data
    combined_plot <- (income_map + income_histogram) / (income_map_log + income_histogram_log)
    
    return(combined_plot)
  }
  
  # Create plots for each race (White, Black, Hispanic, Asian) and filter NAs for each race
  white_plots <- create_plots_for_race("per_capita_income_whiteE", "White", "navy", "lightblue")
  black_plots <- create_plots_for_race("per_capita_income_blackE", "Black", "darkred", "pink")
  hispanic_plots <- create_plots_for_race("per_capita_income_hispanicE", "Hispanic", "darkgreen", "lightgreen")
  asian_plots <- create_plots_for_race("per_capita_income_asianE", "Asian", "darkorange", "orange")
  
  # Print the combined plots for all races
  print(white_plots)
  print(black_plots)
  print(hispanic_plots)
  print(asian_plots)
}

# List of states to generate data and figures for
create_income_maps_and_histograms("PA")
## Getting data from the 2016-2020 5-year ACS

create_income_maps_and_histograms("OH")
## Getting data from the 2016-2020 5-year ACS

create_income_maps_and_histograms("TN")
## Getting data from the 2016-2020 5-year ACS

create_income_maps_and_histograms("WV")
## Getting data from the 2016-2020 5-year ACS

create_income_maps_and_histograms("KY")
## Getting data from the 2016-2020 5-year ACS

create_income_maps_and_histograms("IN")
## Getting data from the 2016-2020 5-year ACS

create_income_maps_and_histograms("IL")
## Getting data from the 2016-2020 5-year ACS

ACS Per Capita Income Data (county & state level)

# List of states for which to retrieve data
states <- c("OH", "PA", "TN", "WV", "KY", "IN", "IL")

# Define the variables to retrieve from the ACS
variables_to_get <- c(
  population = "B01003_001",               # Total population
  population_black = "B02001_003",         # Total Black population
  population_white = "B02001_002",         # Total White (non-Hispanic) population
  population_hispanic = "B03003_003",      # Total Hispanic population
  population_asian = "B02001_005",         # Total Asian population

  per_capita_income_black = "B19301B_001",   # Per capita income for Black population
  per_capita_income_black_moe = "B19301B_001M",  # MOE for Black per capita income
  per_capita_income_white = "B19301H_001",   # Per capita income for White (non-Hispanic) population
  per_capita_income_white_moe = "B19301H_001M",  # MOE for White per capita income
  per_capita_income_hispanic = "B19301I_001",# Per capita income for Hispanic population
  per_capita_income_hispanic_moe = "B19301I_001M",  # MOE for Hispanic per capita income
  per_capita_income_asian = "B19301D_001",   # Per capita income for Asian population
  per_capita_income_asian_moe = "B19301D_001M"  # MOE for Asian per capita income
)

# Function to retrieve and process data for each state
retrieve_data_for_county <- function(state_abbreviation) {
  # List all counties in the state
  state_counties <- list_counties_by_state(state_abbreviation)
  
  # Retrieve ACS data for the state's counties
  state_data <- get_acs(
    geography = "county",
    variables = variables_to_get,
    state = state_abbreviation,
    county = state_counties,
    geometry = TRUE,
    output = "wide",
    year = 2020
  ) %>%
    select(-NAME) %>%
    st_transform(crs = 5070) %>%
    mutate(state = state_abbreviation)
  
  return(state_data)
}

# List of counties by state
list_counties_by_state <- function(state_abbreviation) {
  fips_codes %>%
    filter(state == state_abbreviation) %>%
    pull(county)
}

# Retrieve data for all selected states
all_data <- lapply(states, retrieve_data_for_county)

# Combine data for all counties by state
percapita_income_combined_data_county <- bind_rows(all_data)

# Function to retrieve and process data for each state
retrieve_data_for_state <- function(state_abbreviation) {
  # List all counties in the state
  state_counties <- list_counties_by_state(state_abbreviation)
  
  # Retrieve ACS data for the state's counties
  state_data <- get_acs(
    geography = "state",
    variables = variables_to_get,
    state = state_abbreviation,
    county = state_counties,
    geometry = TRUE,
    output = "wide",
    year = 2020
  ) %>%
    select(-NAME) %>%
    st_transform(crs = 5070) %>%
    mutate(state = state_abbreviation)
  
  return(state_data)
}

# List of counties by state
list_counties_by_state <- function(state_abbreviation) {
  fips_codes %>%
    filter(state == state_abbreviation) %>%
    pull(county)
}

# Retrieve data for all selected states
all_data <- lapply(states, retrieve_data_for_state)

# Combine data for all states
percapita_income_combined_data_state <- bind_rows(all_data)


percapita_income_combined_data_county 
## Simple feature collection with 619 features and 20 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 378600 ymin: 1342000 xmax: 1782000 ymax: 2295000
## Projected CRS: NAD83 / Conus Albers
## First 10 features:
##    GEOID populationE populationM population_blackE population_blackM
## 1  39043       74419          NA              6779               403
## 2  39093      309134          NA             25104               911
## 3  39061      815790          NA            208444              1648
## 4  39007       97416          NA              3782               492
## 5  39167       60217          NA               794               150
## 6  39123       40557          NA               524               109
## 7  39079       32440          NA               326               149
## 8  39035     1241475          NA            364280              1820
## 9  39075       43954          NA                63                51
## 10 39095      430319          NA             81339              1500
##    population_whiteE population_whiteM population_hispanicE
## 1              62990               552                 3385
## 2             260049              1032                31425
## 3             542980              2030                28022
## 4              89870               471                 4269
## 5              57606               141                  666
## 6              38426               249                 2105
## 7              31308                97                  360
## 8             761770              2529                76316
## 9              43196               123                  432
## 10            306681              1325                31680
##    population_hispanicM population_asianE population_asianM
## 1                    NA               578                90
## 2                    NA              3558               324
## 3                    NA             22196               842
## 4                    NA               453                81
## 5                    NA               452                84
## 6                    NA                99                45
## 7                    NA                 0                26
## 8                    NA             38471               890
## 9                    NA               134               122
## 10                   NA              7376               377
##    per_capita_income_blackE per_capita_income_blackM per_capita_income_whiteE
## 1                     17598                     1798                    37675
## 2                     18031                     1474                    35144
## 3                     21433                      624                    43872
## 4                     12824                     2437                    26591
## 5                     16966                     6633                    30987
## 6                     19797                     7732                    39522
## 7                     18898                     9099                    25389
## 8                     20382                      341                    43115
## 9                     10832                     9983                    24777
## 10                    19408                      805                    34491
##    per_capita_income_whiteM per_capita_income_hispanicE
## 1                      2471                       20509
## 2                       828                       18849
## 3                       732                       25788
## 4                      1074                       18318
## 5                      2081                       15058
## 6                      5079                       30165
## 7                      1843                       14333
## 8                       506                       21470
## 9                      1330                       39801
## 10                      729                       18477
##    per_capita_income_hispanicM per_capita_income_asianE
## 1                         2808                    40996
## 2                         1671                    31329
## 3                         2475                    42874
## 4                         6232                    49647
## 5                         4685                    19133
## 6                         8115                   104428
## 7                         2578                       NA
## 8                         1898                    43310
## 9                        14346                       NA
## 10                        1095                    32771
##    per_capita_income_asianM state                       geometry
## 1                     13784    OH MULTIPOLYGON (((1093937 214...
## 2                      5343    OH MULTIPOLYGON (((1128392 212...
## 3                      3612    OH MULTIPOLYGON (((954051 1856...
## 4                     15325    OH MULTIPOLYGON (((1233299 217...
## 5                     12263    OH MULTIPOLYGON (((1203958 190...
## 6                     66216    OH MULTIPOLYGON (((1095010 213...
## 7                        NA    OH MULTIPOLYGON (((1129149 184...
## 8                      2432    OH MULTIPOLYGON (((1160561 212...
## 9                        NA    OH MULTIPOLYGON (((1151123 204...
## 10                     5408    OH MULTIPOLYGON (((1061077 215...
percapita_income_combined_data_state
## Simple feature collection with 7 features and 20 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 378600 ymin: 1342000 xmax: 1782000 ymax: 2295000
## Projected CRS: NAD83 / Conus Albers
##   GEOID populationE populationM population_blackE population_blackM
## 1    39    11675275          NA           1442655              5444
## 2    42    12794885          NA           1419582              6251
## 3    47     6772268          NA           1128806              2966
## 4    54     1807426          NA             64285              1431
## 5    21     4461952          NA            361230              2429
## 6    18     6696893          NA            631923              3881
## 7    17    12716164          NA           1796660              5523
##   population_whiteE population_whiteM population_hispanicE population_hispanicM
## 1           9394878              4978               459939                  251
## 2          10155004              8675               971813                  108
## 3           5196680              4158               377162                  160
## 4           1672255              1658                28679                  422
## 5           3848305              3113               167949                  595
## 6           5510354              4802               475475                  244
## 7           8874067             11289              2190696                  236
##   population_asianE population_asianM per_capita_income_blackE
## 1            268527              2412                    21520
## 2            449320              2567                    22440
## 3            122897              1790                    22143
## 4             14228               743                    20223
## 5             68139              1184                    21623
## 6            158705              2039                    21379
## 7            709567              3537                    23954
##   per_capita_income_blackM per_capita_income_whiteE per_capita_income_whiteM
## 1                      275                    35192                      149
## 2                      285                    39275                      191
## 3                      345                    33927                      222
## 4                     1106                    27973                      275
## 5                      551                    30608                      225
## 6                      503                    33224                      185
## 7                      301                    44581                      194
##   per_capita_income_hispanicE per_capita_income_hispanicM
## 1                       20742                         579
## 2                       20089                         446
## 3                       17853                         567
## 4                       20871                        1556
## 5                       19047                         662
## 6                       19299                         447
## 7                       21679                         224
##   per_capita_income_asianE per_capita_income_asianM state
## 1                    36982                      821    OH
## 2                    36970                      761    PA
## 3                    39809                     2036    TN
## 4                    36246                     3192    WV
## 5                    34915                     2027    KY
## 6                    32256                     1186    IN
## 7                    44968                      922    IL
##                         geometry
## 1 MULTIPOLYGON (((1093937 214...
## 2 MULTIPOLYGON (((1287712 209...
## 3 MULTIPOLYGON (((514676 1342...
## 4 MULTIPOLYGON (((1155316 176...
## 5 MULTIPOLYGON (((583758 1514...
## 6 MULTIPOLYGON (((687449 1680...
## 7 MULTIPOLYGON (((378605 1916...